Contrast based resolution enhancement for photolithographic processing

ABSTRACT

A contrast-based resolution enhancing technology (RET) determines a distribution of contrast values for edge fragments in a design layout or portion thereof. Resolution enhancement is applied to the edge fragments in a way that increases the number of edge fragments having a contrast value that exceeds a predetermined threshold.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser.No. 10/888,444, filed Jul. 9, 2004, now U.S. Pat. No. 7,293,249, whichis a continuation-in-part of U.S. patent application Ser. No.10/356,382, filed on Jan. 31, 2003, now U.S. Pat. No. 7,013,439, whichclaims the benefit of U.S. Provisional Application No. 60/354,042, filedon Jan. 31, 2002, and U.S. Provisional Patent Application No.60/360,692, filed on Feb. 28, 2002, and which are herein incorporated byreference.

FIELD OF THE INVENTION

The present invention pertains to the field of Resolution EnhancingTechnologies (RET) in photolithography. More particularly, thisinvention relates to using contrast measurements to improve theresolution of features to be created via a photolithographic process.

BACKGROUND OF THE INVENTION

In photolithography, a pattern is transferred onto a surface by shininga light through a mask (or reticle) containing the pattern onto aphotosensitive material covering the surface. The light exposes thephoto-sensitive material in the pattern of the mask. A chemical processetches away either the exposed material or the unexposed material,depending on the particular process that is being used. Another chemicalprocess etches into the surface wherever the photosensitive material wasremoved. The result is the pattern itself, either imprinted into thesurface where the surface has been etched away, or protruding slightlyfrom the surface as a result of the surrounding material having beenetched away.

Photolithography is used for a variety of purposes, such asmanufacturing micro-electromechanical systems (MEMS) devices andintegrated circuits (ICs). For ICs, a silicon wafer goes through severaliterations of processing, each forming a patterned layer of the designeddevice structure on the wafer, forming a new layer over each previouslyformed design layer. The different features formed on each layerinteract electrically to form circuit components, such as transistors,transmission paths, and input/output pads.

Photolithography can make very small components. Huge numbers of smallcircuit components can fit within a given surface area. Currentphotolithography techniques routinely fit millions of circuit componentsonto a single chip. Market pressures, however, continually drive forsmaller components, higher density, and greater functionality.

FIG. 1 illustrates one embodiment of a cross-sectional intensity profile110 of light 120 projecting a feature 130 onto a surface in aphotolithographic process. The surface is covered with a photosensitivematerial. A certain intensity of light, dose 150, sometimes called thethreshold dose, is needed to expose the photosensitive material. Belowdose 150, the material is not adequately exposed to create an image. Inwhich case, the edges 160 of the feature 130 appear at the transitionbetween exposed and unexposed areas of the photosensitive material wherethe intensity profile 110 crosses the dose level 150.

The contrast of an edge is basically the slope of the intensity profileat the threshold dose level. A steeper slope means that the edge is moresharply defined. A gradual slope means that the edge appears less sharp,and small variations in intensity can create large changes in theposition of the edge. The sharper the contrast, the more precision andpredictability there is in edge placement, and the smaller the featurescan be.

If a feature is large compared to the wavelength of the light, theintensity profile tends to be deep and sharp. As the feature size getssmaller however, the intensity profile gets shallower and has a moregradual slope. For instance, FIG. 2 illustrates two more intensityprofiles, profile 210 and profile 230. Profile 210 corresponds to afeature 220 having a feature size that is large compared to a wavelengthof the light. Profile 230 corresponds to a feature 240 having a featuresize that is small compared to the wavelength.

This change of the intensity pattern as feature sizes near or drop belowthe wavelength of the light source creates a number of designchallenges. The projected image no longer identically reflects theshapes of the features in the mask. Edge placement becomes increasinglyless precise, often leading to the ends of lines being cut off and sharpcorners being rounded. Neighboring features become increasinglyinterdependent as their intensity patterns overlap, often causingfeatures to “bleed” into each other or not resolve at all.

An area of study called resolution enhancement technology (RET) isconstantly in development to compensate for, or reduce, these effects innear- or sub-wavelength photolithographic processes. Examples of RETsinclude optical proximity correction (OPC), sub-resolution assistfeatures (SRAFs), off-axis illumination, dipole illumination, and phaseshift masks (PSM).

OPC moves feature edges in a mask, essentially shifting an intensityprofile one way or another to move the projected edge. Other RETs alsochange the position of projected edges, but do so more by changing theshape of the intensity profile than by moving the feature edges.

For instance, SRAFs take advantage of the fact that intensity profilesof neighboring edges influence one another. SRAFs themselves are sonarrow that their intensity profiles are not below the threshold doseand are not resolved—hence the name “sub-resolution.” But, theirintensity profiles can interact with the intensity profiles ofneighboring edges. In which case, SRAFs are features that are added to amask near an existing feature, creating a combined intensity profilewith a different contrast, changing the position of the projected edges.

Off-axis illumination and dipole illumination are also RETs that changeintensity profiles, Dipole illumination is basically an extreme form ofoff-axis illumination. Edges that are oriented perpendicular to theorientation of the illumination have sharper intensity profiles andimage more clearly than if illuminated by an on-axis light source.

PSM takes advantage of the interference characteristics of light, byshifting the relative phases of light passing through adjacent regionson a mask so that interference fringes are formed in the image wherethey overlap.

RETs often use edge classifications to determine what kind ofenhancement to apply to a particular edge. For instance, SRAFs areusually inserted in a design based on spacing. Spacing is the outwarddistance from an edge of a feature to another edge. Different spacingclassifications, or ranges of spacings, often receive different SRAFtreatment.

FIG. 3 illustrates spacing classifications for two features, feature 310and feature 320. Spacing 315 is the distance between edges 330 and 340.In which case, edges 330 and 340 may be assigned to a spacingclassification, or range of spacings, that includes spacing 315. Edge350, however, has no opposing edge. In which case, edge 350 may beassigned to a spacing classification for isolated edges.

In the illustrated embodiment, the two different spacing classificationsreceive different SRAF treatment. Specifically, edges 330 and 340receive SRAF 335 centered between them. Edge 350, on the other hand,receives a pair of SRAFs 355 at some predetermined distances 360 and365.

For OPC, edges are often classified based on length and relation. Forinstance, FIG. 4 illustrates a feature 410 having several different edgeclassifications. Edge fragments at corner 420 may be classified asconvex corner edge fragments, which are pushed out to form serif 425 toreduce the rounding of the corner in the projected image. Edge fragmentsat corner 430 may be classified as concave corner edge fragments, whichare pushed in to form inverted serif 435, also to reduce rounding in theprojected image. Edge fragments at line ends 440 and 450 may beclassified as line end edge fragments, which are been pushed out to formhammer heads 445 and 455, respectively, to reduce line end cut-off inthe projected image.

For dipole illumination, or off-axis illumination, edges are oftenclassified based on orientation. For example, dipole illumination oftenuses two masks. One mask is illuminated with a horizontal dipole and onemask is illuminated with a vertical dipole. Since edges that areoriented perpendicular to the orientation of the dipole have sharperintensity profiles and resolve more clearly, edges are usuallyclassified as either horizontal or vertical and assigned to theappropriate mask. The corresponding space in the opposite mask includesa shield to prevent the area from being exposed by the other mask.

For PSM, edges are often classified so that neighboring features areassigned to different phases to reduce the influence the neighboringedges have on one another. Like dipole illumination, PSM often involvestwo masks, a phase mask and a trim mask. In which case, like dipoleillumination, an edge assigned to one mask will often have acorresponding shield in the other mask.

At best, most classification systems used in resolution enhancementtechnologies (RETs) merely suggest that an edge may benefit from aparticular enhancement. Spacing-based classifications usually only takeinto consideration a fixed number of neighboring edges. Edges that rundiagonally through a design are often difficult to classify to either ahorizontal or a vertical dipole mask. And, features may have complexshapes that are interwoven with multiple neighbors, making it verydifficult to classify edges of neighboring features to different phasesin PSM.

SUMMARY OF THE INVENTION

To improve the manufacturability of layout designs for photolithographicprocessing, the present invention applies one or more resolutionenhancement techniques (RETs) to increase the contrast of edge fragmentsthat comprise at least a portion of the layout design.

In one embodiment, edge fragments are categorized into a range ofcontrast values. An RET is applied to the edge fragments within a rangeof contrast values that maximize the number of edge fragments havingcontrast values above a threshold. An RET can be applied to edgefragments having another range of contrast values to improve the overallcontrast of the layout design.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same become betterunderstood by reference to the following detailed description, whentaken in conjunction with the accompanying drawings, wherein:

FIG. 1 illustrates one embodiment of an intensity profile.

FIG. 2 illustrates another embodiment of an intensity profile.

FIG. 3 illustrates one embodiment of sub-resolution assist features(SRAFs).

FIG. 4 illustrates one embodiment of optical proximity correction (OPC).

FIGS. 5A through 5E illustrate one method of implementing the presentinvention.

FIG. 6 illustrates one distribution of contrast values.

FIG. 7A illustrates an exemplary weighting function for use with thepresent invention.

FIG. 7B illustrates weighted distribution of contrast values.

FIG. 8 illustrates another distribution of contrast values produced witha weighting function.

FIG. 9 illustrates a number of cost functions.

FIG. 10 illustrates a distribution of contrast values where some edgefragments have a contrast that is worse after optimization.

FIG. 11 illustrates one embodiment of a hardware system to implement thepresent invention.

FIG. 12 illustrates one embodiment of a machine-readable medium to storeexecutable instructions to implement the present invention.

FIG. 13 illustrates a histogram of contrast values for a layout design.

FIG. 14 illustrates how the distribution edge fragments having highercontrast values can be improved using an off-axis illumination.

FIG. 15 illustrates a cost function Φ used to optimize an opening anglefor quasar illumination.

FIG. 16 illustrates a cost function Φ used to optimize an inner sigmafor quasar illumination.

FIG. 17 illustrates a number of scattering bar parameters that may beoptimized in accordance with an embodiment of the present invention.

FIG. 18 illustrates a pair of contrast histograms calculated for layoutdesigns with and without scattering bars.

FIG. 19 illustrates an initial, intermediate and final contrastdistributions created with the present invention.

FIG. 20 illustrates contrast distributions produced with weightingfunctions in accordance with an embodiment of the present invention.

FIG. 21 illustrates an improvement made to contrast distributions withthe addition of sub-resolution assist features (SRAFs).

FIGS. 22A-22B illustrate contract distributions using dipoleillumination.

FIG. 23 illustrates contrast distributions obtained with a weak and astrong double dipole exposure.

FIG. 24 illustrates an improvement obtained in the contrast distributionshown in FIG. 23 after model based OPC has been applied to sub-optimalinitial masks.

FIG. 25 illustrates contrast distributions of initial masks generated bythe contrast assisted dipole decomposition.

FIG. 26 illustrates a contrast distribution using contrast based dipoledecomposition followed by model based OPC.

FIGS. 27A and 27B illustrate improvements in contrast distributionsusing the present invention.

FIGS. 28A and 28B illustrate improvements in contrast distributionsunder defocus conditions.

FIGS. 29A and 29B illustrate pattern fidelity changes between a layoutprepared in accordance with the present invention and with no contrastoptimization.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the presentinvention. However, those skilled in the art will understand that thepresent invention may be practiced without these specific details, thatthe present invention is not limited to the depicted embodiments, andthat the present invention may be practiced in a variety of alternateembodiments. In other instances, well known methods, procedures,components, and circuits have not been described in detail.

Parts of the description will be presented using terminology commonlyemployed by those skilled in the art to convey the substance of theirwork to others skilled in the art. Also, parts of the description willbe presented in terms of operations performed through the execution ofprogramming instructions. As well understood by those skilled in theart, these operations often take the form of electrical, magnetic, oroptical signals capable of being stored, transferred, combined, andotherwise manipulated through, for instance, electrical components.

Various operations will be described as multiple discrete stepsperformed in turn in a manner that is helpful for understanding thepresent invention. However, the order of description should not beconstrued as to imply that these operations are necessarily performed inthe order they are presented, nor even order dependent. Lastly, repeatedusage of the phrase “in one embodiment” does not necessarily refer tothe same embodiment, although it may.

Embodiments of the present invention apply resolution enhancementtechnologies (RETs) to edges of features in photolithographic designsbased on contrast classifications of the respective edges to modify theedge's contrast. For example, embodiments of the present invention canbe applied to a photolithographic design on a global basis to modify theoverall contrast of the design.

Contrast is a direct indication of how well an edge will be formed in aprojected image. In which case, contrast classification directlyidentifies edges that will be most difficult to form. Edges with highercontrast appear sharper, allowing for more detailed and smaller featuresizes.

Rather than applying an RET to change edge placement of a projectedimage by shifting an intensity profile, embodiments of the presentinvention apply a RET to modify the contrast of the edge and change theslope of the edge's intensity profile. By improving contrast, edges canbe placed with more precision, potentially leading to better overallenhancement solutions. For example, embodiments of the present inventioncan sharpen an intensity profile prior to applying OPC so that OPC doesnot need to move an edge as far it otherwise would.

Furthermore, embodiments of the present invention can apply virtuallyany RET that can modify contrast. For instance, embodiments of thepresent invention can apply sub-resolution assist features (SRAF),off-axis illumination, dipole illumination, phase shift mask (PSM),extensions such as hammerhead, serifs, etc., positive or negative biasassociated with single or multiple exposures, transmission values forfeatures, optical polarization, and the like.

In general, it is preferred to optimize those RETs that affect all edgefragments, like the illumination type or exposure, before optimizing RETapplied to individual edges such as SRAFs, phase shifts, transmissionvalues, or polarization.

As used herein, the terms edge and edge fragment are interchangeable andrefer in general to any fragment defining a boundary, or a part of aboundary, of a feature in a photolithographic design. These designs, orlayouts, can be represented in a number of data formats. One data formatcommonly used to define such a design is GDS II, in which featurescomprise collections of interconnected points in two-dimensional space.In GDS II, an edge or edge fragment may comprise a pair ofinterconnected points in the two-dimensional space.

FIGS. 5A through 5E illustrate one embodiment of the present inventionfor contrast modification. In general, the present invention applies anRET to an edge based on the edge fragment's contrast class, therebymodifying the edge's contrast. The illustrated embodiment repeatedlyapplies this operation to multiple edges in a design and explores theoverall contrast change for a variety of enhancement options.

The illustrated embodiment includes a number of implementation-specificdetails. Other embodiments may not include all of the illustratedoperations, may perform the operations in a different order, may combineand/or separate one or more of the operations, and may includeadditional operations. Several functions, curves, graphs, and histogramsare mentioned in FIG. 5A through 5E. Examples of many of these areillustrated in subsequent figures. The illustrated embodiment assumesthat the optical conditions, such as focus, dose level, numericalaperture, and the like, have already been determined.

At 501, a photolithographic design is fragmented into edges. Fragmentingcan be done in a number of different ways and often involves addingadditional data points in a design to break up long edges into smallerfragments. With smaller fragments, more accurate and detailedenhancements can be applied, but the trade-off tends to be additionalcomplexity, processing time, etc.

At 502, the process identifies a simulation site in each fragment andcalculates an intensity profile for each fragment at the simulationsite. The calculation can be done in any number of ways. In oneembodiment, the simulation site is at the center of an edge fragment andthe intensity profile is calculated along a perpendicular cut lineintersecting the edge fragment at the simulation point. The calculationmay take into consideration features in the design within an area ofinfluence surrounding the simulation site. The radius of the area ofinfluence can be selected in any number of ways, but is often related tothe optical conditions of the particular system being used. The largerthe radius, the more accurate, and time consume, the calculation.

At 503, the process determines the contrast for each edge fragment. Theillustrated embodiment uses the image log slope (ILS) as an equivalentto contrast. One way to calculate the ILS is to take the log of theintensity profile, and determine the maximum derivative of the logfunction. The derivative is the slope of the function, so the ILS istaken at the maximum slope. Other embodiments may calculate contrast indifferent ways, such as (I_(max)−I_(min))/(I_(max)+I_(min)).Alternatively, image slope can be determined at a predeterminedintensity threshold, such as that the dose threshold required to exposea resist material. To determine the slope, the difference in imageintensity is determined at points slightly outwards and slightly inwardsof the point where the image intensity equals the threshold value.

At 504, the process tags each edge fragment with a contrastclassification depending on the contrast value calculated in 503. Inother words, if an edge fragment has a contrast value that falls into aparticular range of contrasts, that edge is assigned to a contrastclassification corresponding to that range. Any number of contrastclassifications can be used. In general, more classifications andsmaller ranges tend to provide more accuracy, but also more complexity.

In one embodiment, operations 502, 503, and 504 are all part of thefragmenting operation 501. That is, in one embodiment, a contrastcalculation is performed at regular intervals along each edge in thedesign. The contrast value at each interval is compared to the ranges ofcontrast values defined by the contrast classifications. If the contrastvalue switches from one range to another, the edge is fragmented and thenewly formed fragment is tagged with the new contrast classification.This approach is often referred to as model-based fragmenting ormodel-based tagging.

Once the edges have all been tagged, the process counts the number ofedges that have been tagged with each contrast classification at 505.For instance, if there are 20 contrast classes, the process will countthe number of edges assigned to each of the 20 contrast classes.

At 506, an original contrast signature is formed. In the illustratedembodiment, the original contrast signature is distribution of thenumber of fragments versus contrast, presented as a histogram. Forinstance, with 20 contrast classes, there will be 20 data points, eachplotted as a number of edge fragments in a particular contrast range.

At 507, the process determines a threshold contrast. The thresholdcontrast is a minimum desired contrast level. The threshold level can beselected in any number of ways, but is often dependent upon thecomplexity of the design and the quality of optical system being used.

At 508, the process calculates the number of fragments in the originalcontrast signature that meet or exceed the threshold contrast. With 20contrast classes, this is simply a matter of adding the numbers of edgefragments in each contrast class that define contrast ranges in excessof the threshold contrast. More complicated systems, with larger numbersof contrast classes, may take the integral of the original contrast fromthe threshold contrast up to the maximum contrast to determine the areaunder the curve, with the area being equivalent to the number of edgefragments.

At 509, the process selects a resolution enhancement type. This couldinclude selecting a high level RET, such as SRAF, dipole, PSM, or thelike. Each of these high level RETs could also include a number ofvariables from which to select. For instance, within SRAF, operation 509could include selecting the spacing between an SRAF and an edge, thewidth of an SRAF, the number of SRAFs, the spacing separating multipleSRAFs, and the like. Within dipole or PSM, 509 could include selecting,for instance, the distance to which a shield is extended in a secondmask to protect an edge in a first mask.

In the illustrated embodiment, just one RET, or one variable within anRET, can be selected per application of the process. In which case, theprocess would have to restart in order to select another RET, or anothervariable within an RET. In one embodiment, the array of RETs and/orvariables from which to choose is limited by factors such asmanufacturability, optical conditions, etc. For instance, the width ofan SRAF may be fixed in a particular system, so the width variable maynot be eligible for selection.

At 510, a contrast classification is selected. As will be seen below,the process will loop back to 510 multiple times until all of thecontrast classes have been selected and used by the process. Thecontrast classes can be selected in any order.

At 511, a resolution enhancement value is selected. For instance, whereSRAF spacing was chosen back at operation 509, operation 511 couldinclude selecting a particular spacing value. As will be seen below, ifa range of values are available, the process can loop back multipletimes to select and use one value at a time until the range values haveall been used. In one embodiment, the range of values may be limited byfactors such as manufacturability, optical conditions, etc. Forinstance, the spacing increments for SRAFs may be fixed in a particularsystem to a certain value, so only certain spacings may be eligible forselection.

At 512, each edge fragment that is assigned to the selected contrastclassification is modified with the selected RET and the selected RETvalue. For instance, each edge in the selected contrast class mayreceive an SRAF at a particular spacing and width. Those outside theselected classification are not assigned the selected RET value that is,they are left uncorrected.

At 513, the process recalculates the contrast (the ILS in thisembodiment) for all of the edges, both in the selected contrast classand outside the selected contrast class. Then, the edges are alltemporarily retagged to the appropriate contrast classes based on theircurrent contrast values at 514. At 515, after all the edges have beenretagged, the number of edges in each class are counted again to form acurrent contrast signature comprising a new histogram of fragmentsversus contrast.

At 517, the illustrated embodiment applies a weight function to thecurrent contrast signature. The weight function can be used to emphasizethe number of edges having large contrast values so that slightdifferences are easier to detect in later comparisons, as discussedbelow. Other embodiments may not use a weight function.

At 518, the process calculates the number of fragments in the currentsignature that meet or exceed the threshold contrast. As with theoriginal signature, this operation could include integrating to get thearea under the curve. Of course, the weight function can substantiallyinflate the number of fragments.

At 519, the process calculates a difference between the original numberof fragments that exceed the threshold contrast and the current numberof fragments that exceed the threshold contrast in the current, weightedcontrast signature.

At 520, the difference is used as a data point in a cost function curve.Each iteration through the range of RET values that are applied to theedges in the selected class process adds another data point to the costfunction. The curve is a function of the difference between the twosignatures versus the enhancement value selected at 511. Differentcurves will be created with each curve corresponding to a differentcontrast class.

At 521, the process checks for additional enhancement values. Forinstance, if a range of SRAF spacings are available, and not all of thespacings have been used yet, the process will loop back to 511 to selectanother value. The process will loop through 511 to 521 until all of thevalues have been used. A data point will be added to a curvecorresponding to the currently selected contrast class for each passthrough 520. Each iteration will revert back to the original design sothat each data point represents the contrast improvement over theoriginal design for just one enhancement applied to just one contrastclass.

When no more values are left at 521, the process proceeds to 522 whereit checks for additional contrast classes. If at least one additionalclass remains to be used, the process loops back to 510 to select a newclass. The process will iterate through 510 to 522 until all of thecontrast classes have been used, and, for each new class, the processwill iterate through 511 to 521 for each enhancement value. Eachiteration through 522 will create a new cost function curvecorresponding to a new contrast class, with a new data point being addedto a given cost function curve for each pass through 520.

When no more contrast classes are left at 522, the process proceeds to523 to select one of the cost function curves. The curves can beselected in any order. At 524, the process identifies a “best” datapoint in the cost function curve indicating the largest global contrastincrease over the original contrast signature while using the smallestresolution enhancement value. That is, if two data points in a curveindicate the same amount of improvement, the data point corresponding tothe smaller enhancement value is selected. In the case of SRAF spacing,the smaller enhancement value would be the shorting, or closer, spacingvalue. In the case of SRAF width, the smaller value would be thenarrower width. Similarly, in the case of the number of SRAFs, thesmaller value would be the lower number of SRAFs.

At 525, the process checks for additional curves and loops through 523and 524 until the best data point is identified in the cost functioncurve associated with each classification. Then, at 526, the processprioritizes the best data points for each cost function curve, from thedata point providing the most global contrast improvement to the datapoint providing the least contrast improvement. Here is where the weightfunction from operation 517 can help. The weight function emphasizes thedifferences between data points to more easily distinguish levels ofcontrast improvement. In one embodiment, a tie can be resolved in thepriority order by favoring a lower contrast class. That is, if the bestdata point from two contrast classes provide the same global contrastimprovement, it is usually more beneficial to give priority to the lowercontrast.

At 527, a resolution enhancement that corresponds to the highestpriority data point is selected. At 528, the selected resolutionenhancement is applied to the edge fragments in the original design thatare tagged with the respective contrast classification. At 529, theprocess checks for additional cost functions that may have been computedto optimize another RET parameter, and loops back to 527 if there isanother. In this second iteration, the second highest priorityenhancement is applied to the design in addition to the previousenhancement. Similarly, the enhancement for the third iteration, ifpresent, is applied in addition to the first and second enhancements.The process continues to iterate through 527 to 529 until all of thedata points for all cost functions computed have been used to applyenhancements to the design in the priority order.

Once all of the enhancements are applied, the global contrast signatureof the design is likely to have improved substantially. However, sincethe enhancements are evaluated individually by the process, but multipleof enhancements are ultimately applied together, some enhancements arelikely to conflict and degrade the contrast in at least a small numberof edges.

In which case, at 530, after all the enhancements have been applied, theprocess selects an edge fragment in the modified design, calculates anintensity profile and contrast for the edge fragment at 531, andcompares the current contrast to the original contrast for that edge. At533, if the contrast has improved, the process checks for more fragmentsat 535 and loops back to 530 for each edge fragment in the design. At533, for any edge fragment for which the current contrast is notimproved over the original contrast, the process does one of two thingsat 534. In one embodiment, the process simply removes any enhancementthat had been applied to the edge fragment in question. Often times, byremoving the enhancement, the edge's contrast will substantially revertback to the original contrast. The process can also add the edge to asubset of edges to be re-processed. That is, the entire process of FIGS.5A through 5E can be repeated, using the design as it was modified inthe first pass through the process as the “new” original design. Thesecond pass can explore additional enhancements for the subset ofproblematic edges. For instance, if the process explored SRAF spacing inthe first pass, then the second pass could explore SRAF width, or thenumber of SRAFs, or the process could switch to an entirely differentRET.

At 536, the last operation in the process is to apply model-based OPC tothe design. Since the process is likely to have improved the edgecontrast over much of the design, OPC will generally operate faster andarrive at a superior solution than it otherwise would.

Other embodiments may switch the operations 510 and 511. That is, ratherthan iterating through a range of enhancement values for each contrastclass, an alternate embodiment may iterate through a range of contrastclasses for each enhancement value.

If the selected RET fails to improve the global contrast to asatisfactory level, or if a user simply wants to explore other RETs forpotentially better solutions, the process can be restarted and anotherRET or variable can be selected at 509.

Various embodiments of the present invention can be applied to virtuallyany RET and virtually any variable within an RET. For instance, for adouble exposure RET, the process can be performed twice, once for eachexposure. For example, in dipole illumination, an original signature canbe determined for a horizontal dipole and enhancements can be exploredwith respect to the horizontal original signature. Another originalsignature can be determined for a vertical dipole and enhancements canbe explored with respect to it. All of the data points can be comparedfrom both exposures and a best data point can be selected for eachcontrast class.

FIG. 6 through 10 illustrate some examples of the various functions andcurves mentioned in FIGS. 5A through 5E. In FIG. 6, a contrast signature610 is a histogram plotted as a function of the number of edges percontrast value. Contrast signature 610 includes edges to either side ofthreshold contrast 620. In other words, the edges that have acceptablecontrasts are those occupying area 630 under the curve 610 and to theright of threshold contrast 620. The edges to the left of thresholdcontrast 620 have unacceptably low contrasts. In which case, oneembodiment of the present invention would start by exploring RETs forthe unacceptably low edges. Another embodiment of the present inventionmay only explore RETs for the unacceptably low edges.

FIG. 7A illustrates one example of a weight function 705. By taking thedot product of weight function 705 with a contrast signature, such assignature 610 from FIG. 6, a weighted contrast signature such as shownin FIG. 7B is produced. FIG. 8 illustrates another example of a weightedcontrast signature 810. The weight function emphasizes the high contrastedges “magnifying” the area 820 under the curve 810 and to the right of620. This magnification can make it easier to recognize the differencesbetween two different cost functions.

FIG. 9 illustrates one example of a set of cost function curves. Each ofthe curves 910, 920, and 930 corresponds to a different contrastclassification. Each data point in a curve represents the differencebetween the number of edges with acceptable contrast in the originalcontrast signature, such as area 630, and a current weighted, contrastsignature, such as area 820. In the illustrated embodiment, points 911and 912 provide the same contrast improvement, but point 911 uses asmaller enhancement value. Therefore, point 911 will generally beselected over point 912.

Similarly, both curves 920 and 930 include the same data point 922. Thatis, the best data point in both curves provides the same contrastimprovement. In order to prioritize the data points, one embodiment ofthe present invention grants a higher priority to the data pointcorresponding to the lower contrast classification. In which case,assuming curve 930 corresponds to a lower contrast classification, theenhancement indicated by data point 922 will be applied to the contrastclass of curve 930 before the contrast class of curve 920.

FIG. 10 illustrates one example where the contrast for some edges isworse after all the enhancements are applied. For instance, curve 1020represents the original contrast signature and curve 1010 represents thecontrast signature after all of the enhancements have been applied.Curve 1010 includes a “tail” 1030 where the contrast is worse than inthe original curve 1020. As discussed above, various embodiments of thepresent invention may eliminate the enhancements from the edges in tail1030, and/or assign those edges to a subset of edges and run themthrough the process again with a different RET selection.

FIG. 11 illustrates one embodiment of a hardware system intended torepresent a broad category of computer systems such as personalcomputers, workstations, and/or embedded systems. In the illustratedembodiment, the hardware system includes processor 1110 coupled to highspeed bus 1105, which is coupled to input/output (I/O) bus 1115 throughbus bridge 1130. Temporary memory 1120 is coupled to bus 1105. Permanentmemory 1140 is coupled to bus 1115. I/O device(s) 1150 is also coupledto bus 1115. I/O device(s) 1150 may include a display device, akeyboard, one or more external network interfaces, etc.

Certain embodiments may include additional components, may not requireall of the above components, or may combine one or more components. Forinstance, temporary memory 1120 may be on-chip with processor 1110.Alternately, permanent memory 1140 may be eliminated and temporarymemory 1120 may be replaced with an electrically erasable programmableread only memory (EEPROM), wherein software routines are executed inplace from the EEPROM. Some implementations may employ a single bus, towhich all of the components are coupled, or one or more additional busesand bus bridges to which various additional components can be coupled.Those skilled in the art will be familiar with a variety of alternateinternal networks including, for instance, an internal network based ona high speed system bus with a memory controller hub and an I/Ocontroller hub. Additional components may include additional processors,a CD ROM drive, additional memories, and other peripheral componentsknown in the art.

In one embodiment, the present invention, as described above, isimplemented using one or more hardware systems such as the hardwaresystem of FIG. 11.

Where more than one computer is used, the systems can be coupled tocommunicate over an external network, such as a local area network(LAN), an internet protocol (IP) network, etc. In one embodiment, thepresent invention is implemented as software routines executed by one ormore execution units within the computer(s). For a given computer, thesoftware routines can be stored on a storage device, such as permanentmemory 1140.

Alternately, as shown in FIG. 12, the software routines can be machineexecutable instructions 1210 stored using any machine readable storagemedium 1220, such as a diskette, CD-ROM, magnetic tape, digital video orversatile disk (DVD), laser disk, ROM, Flash memory, etc. The series ofinstructions need not be stored locally, and could be received from aremote storage device, such as a server on a network, a CD ROM device, afloppy disk, etc., through, for instance, 110 device(s) 1150 of FIG. 11.

From whatever source, the instructions may be copied from the storagedevice into temporary memory 1120 and then accessed and executed byprocessor 1110. In one implementation, these software routines arewritten in the C programming language. It is to be appreciated, however,that these routines may be implemented in any of a wide variety ofprogramming languages.

In alternate embodiments, the present invention is implemented indiscrete hardware or firmware. For example, one or more applicationspecific integrated circuits (ASICs) could be programmed with one ormore of the above described functions of the present invention. Inanother example, one or more functions of the present invention could beimplemented in one or more ASICs on additional circuit boards and thecircuit boards could be inserted into the computer(s) described above.In another example, field programmable gate arrays (FPGAs) or staticprogrammable gate arrays (SPGA) could be used to implement one or morefunctions of the present invention. In yet another example, acombination of hardware and software could be used to implement one ormore functions of the present invention in order to produce a layoutdescription having one or more RETs selected to optimize the number ofedge fragments having a contrast value that is above a predeterminedthreshold.

Thus, a contrast-based resolution enhancing technology is described.Whereas many alterations and modifications of the present invention willbe comprehended by a person skilled in the art after having read theforegoing description, it is to be understood that the particularembodiments shown and described by way of illustration are in no wayintended to be considered limiting.

As indicated above, the present invention provides a framework for theanalysis and characterization of the efficacy of any resolutionenhancement technique (RET) in lithography. The method is based onextracting a distribution of the image log slope (ILS) for a givenlayout under a predefined set of optical conditions. This distributionis then taken as the optical signature for the image local contrast ofthe design. The optical signature can be created for an entire layout,or only for certain cells believed to be problematic. Alternatively, themethod can be used on a test cell containing features commonly found ina layout design with the results of the test cell applied to layoutdesign. Comparisons can be made between the optical signatures generatedusing different illumination/RET strategies. In the embodimentsdescribed below, the method is used to evaluate and optimize twodifferent RET approaches: sub-resolution assist features (SRAF) anddouble exposure dipole illumination.

Traditional model-based OPC techniques improve the overall patternfidelity of a lithographic image by minimizing edge placement errors(EPE). This technique has proven to be quite effective and commerciallysuccessful for 150 and 130 nm generations of IC technology. Imageproperties, however, are more traditionally evaluated by contrast andthe ability to transfer modulation. When transferring images into highcontrast photoresist, some of the image contrast lost in questionableimaging can be recovered, but the entire lithography process is betterachieved if the original image has robust contrast to begin with.

Up to now, overall contrast throughout the design layout has beenachieved by using smaller wavelengths. With the acceleration of processgenerations as described in the International Technology Roadmap forSemiconductors (the ITRS), very aggressive approaches have been examinedfor their potential utility. Almost all of these techniques involvemaking a radical alteration to the layouts that are finally written onthe reticle. Several involve double exposures of multiple reticles, eachof which does not look anything like the desired pattern on the wafer.Software tools have evolved to manipulate the pattern data required forthe creation of these extreme deviations from a WYSIWYG approach,relying on sophisticated image simulation engines embedded in the EDAtools. The simulators predict what the wafer image will look like, andmake the appropriate correction.

To evaluate these simulated images from the lithographer's perspective,there are two requirements for a stable lithographic process: PatternFidelity and Image Transfer Robustness. The first is related to thedesign—stored in a layout as polygonal shapes—that must be transferredto a given substrate preserving the intended functionality of thedevices. The second requirement, Image transfer robustness, as importantas the first, has been studied in great detail, giving birth to thenotion of common process window. If the image-transfer can only bereproduced under a very limited set of dose and exposure conditions—asmall process window—, the process cannot be implemented in the realworld, where small variations are always present and difficult tocontrol at the level of precision required with current hardware.

Pattern fidelity has normally been addressed by first generation OPC(rule and model based). These methods do not generate additionalpolygons in the layout, but typically only move existing edges toachieve a given pattern fidelity. Second generation approaches add newfeatures to the design in various ways. For example, sub-resolutionassist features (SRAF) are additional non-printing structures placedparallel to original edges in various ways. They are typically insertedto allow isolated features to diffract light like dense features,reducing the Iso/Dense bias that can be a common lithographic problem.

Geometric rule-based approaches can return acceptable results when it ispossible to distinguish how a particular feature will behave opticallyfrom purely geometric arguments. This is especially true in thetraditional lithography (k₁>0.7) where feature-size and featureseparation correlate well with contrast and other optical quantities.However in the deep sub-wavelength regime there are many instances inwhich the optical environment changes the optical behavior ofgeometrically equivalent features (e.g. critical features close tolanding pads, 45° edges or low aspect ratio structures).

The method of the present invention classifies edges based on their ownoptical behavior. By using this approach, it is possible to generatemasks that return a higher contrast image with acceptable edge placementerror. The key parameter for classification is not based on EPE, asdetermined from the local image intensity, but based on local imagecontrast, as determined from the image log-slope (ILS). Optimization ofthe layout patterns for OPC is therefore not calculated by minimizingEPE, but my maximizing local contrast. Yet, OPC is needed in order toattain the target CD. By combining an RET optimization and OPC, thelayout is closer to meet the robustness and resolution criteria requiredto any process worthy technology.

In one embodiment of the invention, contrast is defined as the maximumimage log-slope in the vicinity of a given edge:

$\begin{matrix}{C = \left( \frac{{\mathbb{d}\ln}\; I}{\mathbb{d}x} \right)_{\max}} & (1)\end{matrix}$

Where C is Contrast, I is aerial image intensity, x is position.

Normalized Image Log-Slope (NILS) is a function well suited tocharacterize the printability of given features.

$\begin{matrix}{{NILS} = {{\frac{CD}{I_{maskEdge}}\left( \frac{\mathbb{d}I}{\mathbb{d}x} \right)_{maskEdge}} = {{CD}*\left( \frac{{\mathbb{d}\ln}\; I}{\mathbb{d}x} \right)_{maskEdge}}}} & (2)\end{matrix}$

Where, CD=Critical Dimension (Line width), I=Intensity, x=Position

However, due to the monotonic behavior of the optical intensityfunctions, both definitions are equivalent from the maximization pointof view. More importantly, in real layouts there is no single value ofCD, and the positions of features on the final mask after OPC may nolonger be located at the original feature position. By collecting theentire mask related information solely in the aerial image response, wecan derive functions that can be extended to multiple exposures,arbitrary decomposition schemes, and are not coupled with the resistproperties. In addition to this, a lithographic process that is centeredat the threshold of the maximum log-slope delivers maximum exposurelatitude. When optimization of the contrast (Equation 1) is conductedslightly out of focus, it also increases aerial image process window(the area below NILS/defocus curve), thus benefiting focus latitude aswell.

It has been shown that neighboring edges contribute differently to thecontrast and MEEF (Mask Error Enhancement Factor, a measure of thesensitivity of an image to mask errors) of a given edge, and that thestrength of any individual effect depends on the optical system that isbeing used. Since the introduction of the additional features (i.e. SRAFor dipole masks) changes the optical environment, the effect of suchadditional features on the neighboring original edges is substantial.

The determination of image contrast is a calculation very similar tothat needed to determine EPE, and does not require significantadditional computation time. Once the general optical conditions (λ, NA,illumination) have been determined for a given lithography system, thederivative of the image intensity can be easily calculated once theimage of a layout has been simulated. Since it is very difficult tovisualize every edge in a layout tool, aggregations of data can beconveniently presented in histograms, just as histograms of EPE can beused to evaluate pattern fidelity. The histogram represents thedistribution of contrast values found throughout the image, with one“count” occurring for each contrast value (or ILS) uniquely assigned toeach edge fragment. This histogram corresponds to the optical signatureof a particular layout under a predetermined set of optical conditions.

FIG. 13 is an example of such a histogram. In this case, we are seeingthe histogram for a large DRAM cell with more than 5200 opticallydifferent edges. At this point, no RET or OPC has been applied to thelayout, meaning that this contrast distribution is a reference point forour subsequent simulations after the application of various RETs. It isimportant to mention, that the total number of fragments in a histogramshould be constant when evaluating the same layout. This conservationprinciple has been used consistently in all simulations performed.

These original contrast values are a set of discrete bins with aresolution that can be varied—The finer the grid, the larger the numberof bins (and the smaller the values of count are for each bin)—. The setof bins is denoted by C₀, and the counts associated with these valuesare denoted by H₀=f₀ (C₀). We now make the layout manipulations that arerequired for the implementation of a particular RET recipe (e.g. addingSRAF, parsing for dipole illumination, etc.) After specific variationsof the given RET have been added to the layout, a new image issimulated, new contrast values calculated for each edge, and a newglobal contrast distribution H_(RET)=f_(RET) (C₀) is produced. Anexample of a histogram produced with the application of a form ofoff-axis illumination called Quasar illumination for the same layoutused in FIG. 13 is shown in FIG. 14.

It is clear that H_(RET) can represent many things, depending on the RETconditions applied, and is a function dependent on many, many variables.There is therefore an actual family of histograms, {H^(i) _(RET)}depending on a particular variable that is changed. However, for eachRET, there are often a few key parameters. For example, when adding anSRAF, the spacing of the SRAF is a critical parameter that requiresoptimization. Likewise, for dipole illumination, the orthogonal edgebias becomes another key parameter to explore. Even optical parameterssuch as sigma and other pupil dependent quantities can be explored usingthis approach.

In one embodiment of the invention, H_(RET) is computed at variousvalues of these parameters and used to maximize the number of fragmentsthat has a printable contrast. Unfortunately, there is not a simpledefinition of what is printable. This will always be process specific.This threshold contrast value is denoted as C_(T). For the examplespresented here, values of C_(T)>10 represent a healthy printability.

To actually optimize, a cost function Φ is generated as a function ofthe RET technique and the key parameter α under optimization. Thisrepresents the subtraction of sub-optimal contrast counts from thenumber of counts at acceptable contrast.

$\begin{matrix}{{\Phi\left( {a,C_{o}} \right)} = {{\sum\limits_{C_{i} = C_{T}}^{C_{i} = C_{\max}}{H_{RET}\left( {a,C_{o},C_{i}} \right)}} - {\sum\limits_{C_{i} = C_{\min}}^{C_{i} = C_{T}}{H_{RET}\left( {a,C_{o},C_{i}} \right)}}}} & (3)\end{matrix}$By maximizing this function, a more printable image is achieved.

This technique can be further adapted by the addition of a weightingfunction. Although maximization of Φ above will produce a highercontrast image, some edges with extremely low contrast may have lowvalues of H_(RET), but even a single failure in the wrong place cancause an IC to fail. To address this practical reality, a weightingfunction W can be introduced. W is multiplied with H_(RET) to producethe final cost function.

$\begin{matrix}{{\Phi\left( {a,C_{o}} \right)} = \begin{matrix}{{\sum\limits_{C_{i} = C_{T}}^{C_{i} = C_{\max}}{{W\left( C_{i} \right)} \cdot {H_{RET}\left( {a,C_{o},C_{i}} \right)}}} -} \\{\sum\limits_{C_{i} = C_{\min}}^{C_{i} = C_{T}}{{W\left( C_{i} \right)} \cdot {H_{RET}\left( {a,C_{o},C_{i}} \right)}}}\end{matrix}} & (4)\end{matrix}$W can take any form. Typically, W can be high for values of low contrastand 1 for acceptable values. W can also be defined a parabola, centeredon C_(T). By selecting different W functional forms, it is possible totrade areas of high contrast with areas of very low contrast.

By proceeding in this fashion, the method now must consider localinteractions between intersecting RET recipes. Once all optimal casesare simultaneously applied for every parameter α, it is required toverify that the local optimizations of different parameters do notcombine to locally degrade contrast. More importantly, after maximizingcontrast, there are very few locations that have the correct placementafter imaging. In order to address the final pattern fidelity of thedesign, a model-based OPC treatment is used on those edges that have notbeen biased.

Once the model based OPC has finished, the resulting layout is evaluatedto assess the improvement in overall contrast of the design. Whereas adirect model-based solution guarantees that the given layout will havean acceptable pattern fidelity, a combination of contrast optimizationand model-based OPC returns the same level of confidence from thepattern fidelity point of view, plus the added advantage of a highercontrast layout less sensitive to dose and focus variations.

Off-Axis Illumination

FIG. 13 illustrates a histogram distribution for an image of a DRAM cellunder conventional illumination conditions: λ=248 nm, α=0.875, andNA=0.7. Because the minimum line widths are at the border of resolution(kl=0,28 for this layout), contrast is unprintable (C<10) for themajority of the edge fragments.

FIG. 14 illustrates the result when using a typical RET, off-axisillumination. By merely changing the illumination to off-axisillumination, in this case a Quasar illumination system withα_(out)=0.875, α_(in)=0.65, and an opening angle of 30°, the entiredistribution has moved to higher contrast, making the image far moreprintable.

FIG. 15 shows how the cost function Φ can be used to determine the bestopening angle for an off-axis illumination system, from the imagecontrast viewpoint. The value of Φ for the conventional case is includedfor reference. As expected, by reducing the opening angle, there aremore high contrast edges, which are translated into a higher value forthe cost function.

FIG. 16 explores how the contrast changes by adjusting the inner sigmaand keeping the opening at 30° and the outer sigma at 0.875. Accordingto these results, the best illumination pupil will be a 30° Quasarillumination, with outer sigma of 0.875 and inner sigma of 0.05. Thereare more considerations for a robust process other than contrast, butthis information suggests the range of settings that will provideadequate imaging from the contrast point of view.

Sub-Resolution Assist Features

Sub-resolution assist features (SRAF) have been used for some time as arule-based technique for OPC that reduces iso-dense bias and increasescontrast and process window. An illustration of a generic SRAF is shownin FIG. 17. As is clear from the diagram, there are several parametersthat define the placement and application of even a simple SRAF. All theparameters that define an assist feature rule have unique advantages andlimitations. One could vary all the SRAF parameters; however, maskmanufacturing and inspection constraints define current limits to someof these values. Current mask manufacturing processes can control thewidth of individual SRAF across the full design, as long as the targetwidth is constant, and the distance between SRAFs (sometimes called barsas well) depends on the pattern recognition capabilities of currentinspection tools.

The present invention is general enough to accommodate any geometricparameter and evaluate its optical performance as described above. Formanufacturability reasons, it was decided that feature-to-SRAF spacingwas in general the best parameter to optimize. This is because the SRAFwidth is often already at the minimum resolution that can be produced bythe mask making process, and once the width of the SRAF is fixed, alongwith the inter-feature distances and minimum aspect ratio geometries,the SRAF separation is the only remaining parameter that can freely bechosen.

SRAF-to-main feature separation alters the contrast of the originaledges without creating many problems during the mask manufacturingprocess so long as the separation lies within the current maskmanufacturing and inspection constraints.

There are many considerations involved in the successful implementationof this method. It is important to remember that since the currentmethodology maximizes the global contrast of the layout, this approachwill inevitably generate a few cases where the OPC recipe is notappropriate for specific topological cases. This design-dependentproblem is explored in more detail elsewhere.

FIG. 18 shows that the thoughtless application of SRAF by a simple rulecan generate few cases with an even lower contrast value. Such cases arelater translated in poor resolution regions where killer defects (suchas bridging or pinching) can occur.

In order to reduce this problem, after the application of global rulesfor SRAF application, a local examination of the SRAF can expose thesituations in which the SRAF maximizes the contrast of one edge but atthe same time disturbs neighboring features. Once those cases areidentified, a local clean up usually can correct the situation. Thereare two possible choices: Remove the SRAF completely or, adjust itlocally.

For reasons discussed above, the bars that caused degradation of thelocal contrast are erased completely. After the bars that degradelocally neighboring edges are removed, a new contrast distribution iscalculated and the impact on the removal of the SRAF quantified. This isshown in FIG. 19. FIG. 19 shows three different curves: the originalcontrast distribution (solid line), the distribution immediately afterglobal optimization (dashed line with clear squares) and the finalcontrast distribution after local correction (solid line and solidsquares). After final cleanup, there are no cases oflower-than-original-contrast edges. These regions have in fact beenfound previously and they depend on the set of optical conditions thatare used.

Up until now a constant weight function W=1 was used during the costfunction evaluation. FIG. 20 compares a case where the weight functionwas altered to a function of the form:W=−2.79·ILS+30.854[0,11]W=1(11,13]W=2.55·ILS−32.703(13,∞)  (5)This way, the final solution is biased towards higher contrast values.Notice how the functional form of both distributions is essentially thesame; only the peaks and valleys are slightly different. Thedistribution that results from using an improved weighing function isclearly better since it generates more high contrast edges. The additionof weights to the distributions is a global control of the allowedtradeoffs between low, nominal and high ILS values. Equation 5 weighslow-ILS values more (ILS from 0 to 11 are considered low in thisexample, but the range depends on the specific process requirements),which in turn returns a smaller cost (Φ). The nominal ILSvalues—contained within the (ILS from 11 to 13 range)—are not weighed,and finally the large ILS range (ILS greater than 13) returns a biggercost when the distribution is shifted toward larger ILS values. Alithographer may find these weights useful when determining the bestform of the cost function (Φ). By assigning different weights, it ispossible to find equivalencies, for example: Tradeoff one two edges ofILS=30 if that move makes one edge with ILS=2 to increase to ILS=3. Itis possible that a variable SRAF approach can prevent the degradation ofthe final global contrast distribution, but such an approach should onlybe explored if the image formation cannot be optimized by any othermeans (for example, re-tuning of the optical illumination conditions) orif mask manufacturing and inspection constraints are relaxed. However,it is encouraging finding that even with the removal of some SRAF, theclass of higher contrast edges remains almost unchanged, and the classof lower contrast edges is not worse than the original.

The main objective of SRAF and many other Resolution EnhancementTechniques (RET) is to improve the process window of a particulardesign. So far the embodiments of the invention described above arefocused on the importance of improving contrast. However, as FIG. 21indicates, by maximizing contrast according to the invention it ispossible to also increase the process window of the design withouthaving to simulate a large set of different dose and focus conditions.

FIG. 21 shows 4 curves: The original contrast distribution at best focus(solid line), the final contrast distribution at best focus (solid linewith square markers), the original layout with no OPC at 0.3 microndefocus (dashed lines) and the layout with the SRAF calculated from thein-focus case but simulated at 0.3 micron defocus (dashed lines andsolid squares). This very simple method is able to increase the numberof cases that can be found closer to higher contrast (and therefore morerobust imaging) regions. Since some SRAF were completely removed, theout-of-focus distributions are very similar to each other at lowcontrast values.

Dipole Illumination

An RET that has recently been considered for the 65 nm node is dipoleillumination. Although the resolution enhancement potential wasoriginally recognized over 10 years ago, it was quickly realized thatdouble exposures would be required to make a complete image, in whichvertical lines are printed with a horizontal dipole and horizontal linesare printed with a vertical dipole. Dipole was abandoned for moregenerally applicable, single exposure techniques like model-based OPC.

With the push to smaller wavelengths, the topic of dipole illuminationhas been reopened. The key problem remains, however, the decompositionof an arbitrary layout into two masks, one for a vertical dipole and onefor a horizontal dipole. The contrast optimization technique of thepresent invention has been found to be extremely helpful for determiningthe efficacy of various decomposition recipes.

In the case of a double exposure dipole illumination (FIG. 22) there aretwo distinctive optical signatures, one related to each dipoleorientations. The notion of vertical and horizontal features isgeneralized to the low and high contrast regions under a particular setof illumination conditions. The ILS that defines a low or high contrastedge is a free optimization variable. FIGS. 22A and 22B show the rangethat contains the majority of edges. For FIG. 22A the majority of edgesare contained in the ILS=[3,10] range. FIG. 22B suggests that themajority of the edges are within ILS=[6,15]. The range that results fromthe sum of both ranges (3,15), creates the set of candidates for the ILSvalue that will be used to decide if a feature has low or high contrastand therefore in which mask it should be controlled. The value of ILS=10was chosen since it is located in the upper half portion of the range,but further optimization may be needed in order to define the best ILSvalue to make such edge selection.

To illustrate the application of the method of the present invention,two optical conditions were chosen as an example: a strong dipole and aweak dipole. All simulations were carried out using stepper conditionsλ=248 urn and NA=0.7. The dipole illumination setting has α_(out)=0.875with a 35° fragment opening. The strong dipole had α_(inner)=0.6 and theweak dipole had α_(inner)=0.35.

As shown in FIG. 22, certain edges exhibit low or high contrastdepending on the rotation of the dipole element. By looking at thedistributions it is possible to determine that the design is mostlyoriented on the horizontal direction. When the design is exposed by ahorizontal dipole, the contrast distributions are shifted toward lowervalues. When the same system is exposed under a vertical dipole, thedistributions shift towards higher contrast values. This behavior iscontrolled, among other things, by the inner sigma of the illuminator.Strong dipole conditions (low inner sigma) enhance the effect previouslydescribed.

However, a dipole method requires two exposures, which means that if noprotections are defined between exposures, there is a reduction of thefinal contrast value that a given design may attain. FIG. 23 is thelower limit of the method. Only one mask is used for both exposures.Under weak dipole conditions the distribution is slightly shifted towardhigher contrast values with respect to the strong dipole case. This hasto do with the strong proximity and 0^(th) order light that degrades thefinal pattern during the non-optimal exposure step. The successiveimprovement in contrast is compared to this limiting case in order toprevent situations where the method proposes a solution that may verywell improve the global contrast, but can locally generate low contrastregions that translate in possible sites for line pinching or bridgingdefects.

A double exposure model-based OPC requires three layers as input: Atarget layer that corresponds to the intended design, and one layer foreach exposure. By manipulating the edges present in both masks themethod converges to a solution that attains the correct pattern fidelityspecified by the target layer. There are many possible solutions to theproblem, especially when the design needs to be split into two or moreexposures. It is possible in principle to feed the original design threetimes to the model-based OPC method (as the target design, and as thefirst and second exposure masks). The method will converge to a solutionthat returns acceptable pattern fidelity for that specific set ofconditions. FIG. 24 shows the final contrast distributions after thisapproach has been used. Both distributions are shifted towards highercontrast values with respect to the lower limiting case presented inFIG. 23. FIG. 24 shows the distributions that the contrast-based methodwill now use as the original distribution, serving as reference forimprovement.

After following the steps described in the previous section, theresulting layouts from the contrast-assisted decomposition are evaluatedto determine the new contrast distribution. The spread of suchdistributions, as long as the spread occurs toward higher contrastvalues, is not undesirable. FIG. 25 shows the contrast distributions forthe weak and strong dipole conditions. These masks layouts are later fedto a model based OPC. Only the edges that have not previously received abias are allowed to move during the method. By doing this, theconvergence criterion is forced to follow a different path in thesolution space and arrive at a different answer that achieves the targetpattern fidelity and improves the global contrast of the design at thesame time.

The final contrast distributions that result from the contrast assisteddecomposition and the model-based OPC, are not necessarily narrower thanthe distributions obtained from applying model-based OPC to asub-optimal initial set of masks. Since this is not a critical dimension(CD) distribution, the spreading of the distributions towards highervalues of contrast is in fact more desirable than a narrow contrastdistribution centered on a lower contrast region. FIG. 26 shows thecontrast distributions that result from the combined use of a modelassisted decomposition and a model based OPC.

FIG. 27 shows the incremental shift of the final contrast distributionsfor two systems: 15A for weak dipole conditions and 15B for strongdipole conditions. When no special processing has been performed on theoriginal design, it is possible to calculate the lower limiting case(dashed lines). When this set of masks is passed to the model-based OPC,new masks are generated. New distributions can be calculated from thisnew set of masks (light lines with square markers). This intermediatesolution has improved the contrast and meets the pattern fidelitycriterion. However, in order to improve further the contrast of thefinal result, one needs to start from a different set of masks. In thismethod, that other set of masks is the result of the contrast-assisteddecomposition. Such masks have distributions (dark continuous line) thathave already shifted to higher contrast values as determined by themethod. These masks very likely do not meet the pattern fidelityconstraints. Because of that, they are only used as initial conditionsfor the model-based OPC correction. Once this final model-based OPC hasbeen applied to the masks generated by the contrast-assisted method; anew distribution, generally better than all previously calculated cases,is obtained (light continuous line).

In principle, by maximizing the contrast of a given layout, it ispossible to obtain a solution that will be less sensitive to defocus anddose variations. In the end, the goal is to improve the overall processwindow of a particular design.

FIG. 28 shows how the solution calculated at best focus responds withvarying focus. The light lines highlight the system that was not subjectto dipole decomposition, while the dark lines indicate the distributionsfor the set of masks that received a contrast-assisted decomposition. Itis expected that the best method should prevent large variations ofcontrast for every edge in the design. FIG. 28A shows that for thestrong dipole condition, the contrast-based method does not shift as faras in the case where no special decomposition was used. At the sametime, the area defined by the overlap between the distributionscalculated at different focus conditions is larger and centered towardshigher values of contrast when the contrast-assisted decomposition isused.

At weak dipole conditions the behavior is similar, although not as clearas when strong dipole conditions are used. The contrast distributionscapture effectively the increments in the process window of a particulardesign. Even these seemly small changes in the contrast behavior of thelayout can make the difference between acceptable and non-acceptablelithographic conditions.

Pattern fidelity is difficult to account for. Traditionally, onedimensional process windows have been used to rank the feasibility of aparticular process. However, such metrics do not necessarily capture thetwo-dimensional aspects present in any real design. A statisticalevaluation of the patterns can be performed as described in detailelsewhere. In this case it was decided to select a specific region ofthe design under study and simulate its optical behavior at two fociconditions and three different reduced threshold values (0.18, 0.20,0.22).

The area of simulation depicted in FIG. 29, indicates that at zerodefocus conditions the system has perfect pattern fidelity, including45° angles and low aspect ratio fragments. However, when the conditionsare other than the ones used for simulation (e.g. at 0.3 μm defocus),different killer defects can be found. The circles highlight regions atwhich there has been strong pattern fidelity degradation.

The solution from the contrast-assisted decomposition method behaves ina much better fashion than when the contrast-assisted solution issubject to the same threshold (dose) and focus variations. When thepattern fidelity is maintained in almost all regions—the variationshighlighted in FIG. 29B (at 0.3 microns defocus) are not as large as inthe previously described case—the method can further be improved byincluding additional features that can minimize the apparent iso-densebias. While sub-resolution assist features were not used in this case,they can be added to the mask following the same contrast enhancementcriteria used to determine the optimal bias values. It is also importantto mention that at the end of this process, all the regions that presentproblems are already identified, reducing the number of edges that needto be inspected and fixed later on.

The method of the present invention maximizes the local contrast forevery edge, as defined by the image log-slope (ILS). While it isbelieved that this metric captures the essential behavior related toimage local contrast, there is no data that suggests that similarresults cannot be achieved or improved by different localcontrast-related functional forms.

By classifying edges based on image log slope, it is possible togenerate rules that in general improve the local contrast of the patternbeing imaged. Furthermore, we have also provided evidence that localcontrast and depth of focus are directly related, and that by improvingILS it is possible to improve the depth of focus. This result isparticularly important since it allows RET rule optimization under afixed set of optical conditions. This finding has an important impact onthe number of simulations and experiments needed to identify an optimalRET approach that guarantees a stable process under focus variations.

Aberration and overlay sensitivity performance are still questions openfor a more detailed analysis. Such questions can be answered viasimulation, but an experimental validation is preferred in order todetermine the actual performance of the method under real conditions.Initial simulations however, present encouraging results for theadoption of dipole masks for sub 100 nm imaging.

The data indicates that designs that contain 45° features can also besuccessfully imaged using this approach.

A complete and integrated approach can accept further restrictions,since the method can often be misused to generate acceptable RET recipesfrom the local contrast point of view, but completely unacceptableresults from the pattern fidelity viewpoint. If mask-manufacturingconstraints can be relaxed, the current approach is able to furtheradjust other geometric parameters that define a complete RET recipe. Itis possible to do so as long as the changes do not become first order.

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the scope of the invention. For example,although the disclosed embodiments employ one or more RETs to increasethe number of edge fragments having a contrast value that is greaterthan a threshold contrast value, it will be appreciated that other testscould be used. For example, it is also possible to apply one or moreRETs to minimize the number of edge fragments having contrast valuesthat are below a threshold value or to adjust a ratio of the number ofedge fragments having contrast values above or below a threshold value.Similarly, the determination of whether RETs should be applied at allcan be made by determining the distribution of uncorrected contrastvalues. It is therefore intended that the scope of the invention bedetermined from the following claims and equivalents thereto.

1. A computer-implemented method of processing a data layout file thatdefines a plurality of features to be created in an integrated device,comprising: using a computer to receive at least a portion of the datalayout file that defines a number of features to be created in theintegrated device; fragmenting each of the features into a number ofedge fragments; tagging each of the edge fragments with a contrastvalue; determining a distribution of contrast values for the tagged edgefragments; and determining if one or more resolution enhancements shouldbe applied to the data layout file by comparing contrast values of thedistribution corresponding to the tagged edge fragments to a thresholdcontrast value.
 2. The method of claim 1, wherein the determining if theone or more resolution enhancements should be applied further comprisesdetermining the number of edge fragments having contrast values that aregreater than the threshold contrast value.
 3. The method of claim 1,wherein the determining if the one or more resolution enhancementsshould be applied further comprises determining the number of edgefragments having contrast values that are lower than the thresholdcontrast value.
 4. The method of claim 1, wherein the determining if theone or more resolution enhancements should be applied further comprisesdetermining the ratio of the number of contrast values that are lowerthan the threshold contrast value to the number of edge fragments thathave contrast values that are higher than the threshold contrast value.5. The method of claim 1, further comprising: applying the one or moreresolution enhancements to the data layout file, thereby creating anupdated data layout file.
 6. A computer readable storage device storinga sequence of programmed instructions that cause a computer system toimplement a method of processing a data layout file that defines aplurality of features to be created in an integrated device, the methodcomprising: receiving at least a portion of the data layout file thatdefines a number of features to be created in the integrated device;fragmenting each of the features into a number of edge fragments;tagging each of the edge fragments with a contrast value; determining adistribution of contrast values for the tagged edge fragments; anddetermining if one or more resolution enhancements should be applied tothe data layout file by comparing contrast values of the distributioncorresponding to the tagged edge fragments to a threshold contrastvalue.
 7. The computer readable storage device of claim 6, wherein thedetermining if the one or more resolution enhancements should be appliedfurther comprises determining the number of edge fragments havingcontrast values that are greater than the threshold contrast value. 8.The computer readable storage device of claim 6, wherein the determiningif the one or more resolution enhancements should be applied furthercomprises determining the number of edge fragments having contrastvalues that are lower than the threshold contrast value.
 9. The computerreadable storage device of claim 6, wherein the determining if the oneor more resolution enhancements should be applied further comprisesdetermining the ratio of the number of contrast values that are lowerthan the threshold contrast value to the number of edge fragments thathave contrast values that are higher than the threshold contrast value.10. A data layout file describing a number of features to be created inan integrated device, the data layout file having been processed by acomputer-implemented method comprising: using a computer to receive atleast a portion of an input file that defines the number of features tobe created in the integrated device; fragmenting each of the featuresinto a number of edge fragments; tagging each of the edge fragments witha contrast value; determining a distribution of contrast values for thetagged edge fragments; and determining if one or more resolutionenhancements should be applied to the data layout file by comparingcontrast values of the distribution corresponding to the tagged edgefragments to a threshold contrast value.
 11. The computer readablestorage device of claim 6, wherein the method of processing a datalayout file further comprises: applying the one or more resolutionenhancements to the data layout file, thereby creating an updated datalayout file.
 12. The data layout file of claim 10, wherein thedetermining if the one or more resolution enhancements should be appliedfurther comprises determining the number of edge fragments havingcontrast values that are greater than the threshold contrast value. 13.The data layout file of claim 10, wherein the determining if the one ormore resolution enhancements should be applied further comprisesdetermining the number of edge fragments having contrast values that arelower than the threshold contrast value.
 14. The data layout file ofclaim 10, wherein the determining if the one or more resolutionenhancements should be applied further comprises determining the ratioof the number of contrast values that are lower than the thresholdcontrast value to the number of edge fragments that have contrast valuesthat are higher than the threshold contrast value.
 15. The data layoutfile of claim 10, wherein the method further comprises: applying the oneor more resolution enhancements to the data layout file, therebycreating an updated data layout file.